{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Day22 进阶构图元素——一页多图（下）\n",
    "\n",
    "　　今天作为**一页多图**内容的**下篇**，我们来学习使用`matplotlib`中自由度超高的`inset_axes()`来随意插入子图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-10T10:30:32.856778Z",
     "iopub.status.busy": "2020-11-10T10:30:32.856778Z",
     "iopub.status.idle": "2020-11-10T10:30:33.071204Z",
     "shell.execute_reply": "2020-11-10T10:30:33.071204Z",
     "shell.execute_reply.started": "2020-11-10T10:30:32.856778Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "matplotlib版本： 3.1.3\n"
     ]
    }
   ],
   "source": [
    "import matplotlib;print('matplotlib版本：', matplotlib.__version__)\n",
    "import matplotlib.pyplot as plt # 从matplotlib中导入我们最经常使用的pyplot子模\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] # 设定默认字体为SimHei以解决中文显示乱码问题\n",
    "plt.rcParams['axes.unicode_minus'] = False # 解决图像负号'-'不正常显示的问题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "　　在之前两天的日程中，我们get到如何基于网格化的方式来创建子图，而在`matplotlib`3.0版本之后，新增了`inset_axes()`方法，通过它我们可以在已有`Axes`画幅的基础上，在指定位置插入指定长和宽的新子图。\n",
    "  \n",
    "　　其`bounds`参数格式为`[x0, y0, width, height]`，其中`x0`与`y0`用于表示新子图左下角坐标，`width`与`height`分别代表新子图的宽与高，在另一个参数`transform`的作用下可分为两种情况："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-10T10:30:33.073199Z",
     "iopub.status.busy": "2020-11-10T10:30:33.072201Z",
     "iopub.status.idle": "2020-11-10T10:30:33.310565Z",
     "shell.execute_reply": "2020-11-10T10:30:33.310565Z",
     "shell.execute_reply.started": "2020-11-10T10:30:33.073199Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.5, 'ax1')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 当transform=ax.transAxes，也就是默认参数时\n",
    "# bounds的四个参数对应为相对ax的比例系数\n",
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "\n",
    "ax.scatter(range(5), range(5))\n",
    "\n",
    "ax1 = ax.inset_axes(bounds=[0.5, 0.5, 0.5, 0.5])\n",
    "ax1.text(0.5, 0.5, 'ax1', va='center', ha='center', fontsize=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-10T10:30:33.311561Z",
     "iopub.status.busy": "2020-11-10T10:30:33.311561Z",
     "iopub.status.idle": "2020-11-10T10:30:33.474127Z",
     "shell.execute_reply": "2020-11-10T10:30:33.474127Z",
     "shell.execute_reply.started": "2020-11-10T10:30:33.311561Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.5, 'ax1')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 当transform=ax.transData时\n",
    "# bounds的四个参数对应坐标轴实际x与y轴的数值量\n",
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "\n",
    "ax.scatter(range(5), range(5))\n",
    "\n",
    "ax1 = ax.inset_axes(bounds=[0.5, 0.5, 0.5, 0.5], transform=ax.transData)\n",
    "ax1.text(0.5, 0.5, 'ax1', va='center', ha='center', fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "　　掌握`inset_axes()`之后，我们就可以制作出很多组合图，譬如可自由设置位置的`colorbar`："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-10T10:30:33.475125Z",
     "iopub.status.busy": "2020-11-10T10:30:33.475125Z",
     "iopub.status.idle": "2020-11-10T10:30:33.622766Z",
     "shell.execute_reply": "2020-11-10T10:30:33.622766Z",
     "shell.execute_reply.started": "2020-11-10T10:30:33.475125Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from matplotlib import ticker\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "\n",
    "x = np.random.uniform(0, 1, 100)\n",
    "y = np.random.uniform(0, 1, 100)\n",
    "colors = np.random.uniform(0, 1, 100)\n",
    "\n",
    "\n",
    "scatter = ax.scatter(x, y, c=colors, cmap='Greens', edgecolor='#00000095')\n",
    "\n",
    "# 为colorbar准备好Axes底盘\n",
    "colorbar_ax = ax.inset_axes(bounds=[0, 1.08, 1, 0.05])\n",
    "\n",
    "\n",
    "# 在创建好的Axes上叠加colorbar\n",
    "fig.colorbar(scatter, colorbar_ax, \n",
    "             orientation='horizontal', \n",
    "             extend='both',\n",
    "             ticks=ticker.MultipleLocator(0.1));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "　　在此基础上，还可以基于`Axes`对象的`indicate_inset_zoom()`方法实现局部区域放大的效果："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-10T10:30:33.623728Z",
     "iopub.status.busy": "2020-11-10T10:30:33.623728Z",
     "iopub.status.idle": "2020-11-10T10:30:33.753381Z",
     "shell.execute_reply": "2020-11-10T10:30:33.753381Z",
     "shell.execute_reply.started": "2020-11-10T10:30:33.623728Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "\n",
    "ax.plot(range(5), range(5))\n",
    "\n",
    "ax.set_xlim(-10, 30)\n",
    "ax.set_ylim(-10, 30)\n",
    "\n",
    "axins = ax.inset_axes([0.5, 0.5, 0.47, 0.47])\n",
    "axins.plot(range(5), range(5))\n",
    "axins.set_xticks([])\n",
    "axins.set_yticks([])\n",
    "\n",
    "ax.indicate_inset_zoom(axins);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 课后测验：\n",
    "\n",
    "　　接下来的时间交给你们，通过本节课的内容，请你利用今天所学到的`inset_axes()`，结合过往所学的一些知识，还原出下图的样子：\n",
    "\n",
    "<center>\n",
    "    <img src=\"imgs/课后题目.png\" width=400></img>\n",
    "</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 请在今天对应的帖子下回复你的代码和结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "for i in range(0,8):\n",
    "    for j in range(0,8):\n",
    "        axins = ax.inset_axes([i*0.125,j*0.125,0.125,0.125])\n",
    "        if (i+j)%2!=0:\n",
    "            axins.set_facecolor('black') \n",
    "        axins.set_xticks([])\n",
    "        axins.set_yticks([])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
